library(tidyverse)
library(cowplot)
library(broom)
library(plotly)
# data
data1 <- data %>%
gather(Sample,Count,2:37)
# Separate samples by identifiers
data2 <- data1 %>%
separate(Sample, into=c("Sample_ID","Dilution_factor",
"Injection","Tech_rep", sep = "_")) %>%
select(-`_`)
# Standards
standards1 <- standards %>%
gather(Sample,Count,2:13)
standards2 <- standards1 %>%
separate(Sample, into=c("Sample_ID","When","Dilution_factor",
"Nano_day","Injection","Tech_Rep", sep = "_")) %>%
select(-`_`)
# Refactoring Columns for samples
data2$Sample_ID <- as.factor(data2$Sample_ID)
data2$Dilution_factor <- as.numeric(data2$Dilution_factor)
data2$Injection<- as.factor(data2$Injection)
data2$Tech_rep <- as.numeric(data2$Tech_rep)
data2
# Refactoring COlumns for key
key$Sample_ID <- as.factor(key$Sample_ID)
key$Animal <- as.factor(key$Animal)
key$Condition <- as.factor(key$Condition)
key
# Refactoring columns for standards
standards2$Sample_ID <- as.factor(standards2$Sample_ID)
standards2$When <- as.factor(standards2$When)
standards2$Dilution_factor <- as.numeric(standards2$Dilution_factor)
standards2$Injection <- as.factor(standards2$Injection)
standards2$Nano_day <- as.numeric(standards2$Nano_day)
standards2
standards2 <- standards2 %>%
mutate(True_Count=Dilution_factor*Count)
# Set the correct order of 'categorical factors'
standards2$Nano_day <- factor(standards2$Nano_day, levels=c('1'))
standards2$When <- factor(standards2$When, levels=c('before','after'))
standards2
standards3 <- standards2 %>%
group_by(particle_size,Sample_ID,When,Dilution_factor,Nano_day,Injection) %>%
summarise( tech_N = length(True_Count),
tech_mean = mean(True_Count),
tech_sd = sd(True_Count),
tech_se = tech_sd/sqrt(tech_N))
standards3
standards4 <- standards3 %>%
group_by(Nano_day,When,particle_size) %>%
summarise( inj_N = length(tech_mean),
inj_mean = mean(tech_mean),
inj_sd = sd(tech_mean),
inj_se = inj_sd/sqrt(inj_N))
standards4
std_plot <- standards4 %>%
ggplot(aes(x=particle_size,y=inj_mean,color=When))+
geom_ribbon(aes(ymin=inj_mean-inj_se, ymax=inj_mean+inj_se),
alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
geom_line(size=2) + xlim(0,500)+ #line size, x-axis scale
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Particle Size") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
labs(color="Condition")+ #Label table title
facet_grid(. ~ When)
std_plot
## Warning: Removed 1000 rows containing missing values (geom_path).
standards_df <- standards4 %>%
group_by(Nano_day,When) %>%
summarise(total=sum(inj_mean))
standards_df
standards_df %>%
ggplot(aes(x=Nano_day,y=total,fill=When))+
geom_col(position="dodge")+
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Experimental Day") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
labs(color="When") #Label table title
Intra.assay_cv <- standards_df %>%
group_by(Nano_day) %>%
summarise(Day_N = length(total),
Day_mean = mean(total),
Day_sd = sd(total),
Day_se = Day_sd/sqrt(Day_N),
Day_cv = Day_sd/Day_mean )
Intra.assay_cv
data2 <- data2 %>%
mutate(True_Count = Dilution_factor*Count)
data2
data3 <- data2 %>%
group_by(particle_size,Sample_ID,Dilution_factor,Injection) %>%
summarise( tech_N = length(True_Count),
tech_mean = mean(True_Count),
tech_sd = sd(True_Count),
tech_se = tech_sd/sqrt(tech_N))
data3
data4 <- data3 %>%
group_by(particle_size,Sample_ID,Dilution_factor) %>%
summarise( inj_N = length(tech_mean),
inj_mean = mean(tech_mean),
inj_sd = sd(tech_mean),
inj_se = inj_sd/sqrt(inj_N))
data4
# Average technical replicates and merge with key
merge <- left_join(key,data3, by= "Sample_ID")
merge
# Average injection replicates and merge with key
merge1 <- left_join(key,data4, by= "Sample_ID")
merge1
sample_plot <- merge %>%
ggplot(aes(x=particle_size, y=tech_mean,color=Injection ))+ #plot
geom_ribbon(aes(ymin=tech_mean-tech_se,
ymax=tech_mean+tech_se),
alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
geom_line(size=2.0) + xlim(0,500)+ #line size, x-axis scale
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Particle Size") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
labs(color="Injection")+ #Label table title
facet_grid(Animal ~ Condition)+
geom_vline(xintercept = 140)+
annotate("text", x= 235, y = 1E9, label= "140nm")
sample_plot
merge2 <- merge1 %>%
group_by(Animal,Condition) %>%
summarise(particle_conc=sum(inj_mean))
merge2
merge3 <- merge2 %>%
group_by(Condition) %>%
summarise(Condition_N=length(particle_conc),
Condition_mean = mean(particle_conc),
Condition_sd = sd(particle_conc),
Condition_se = Condition_sd/sqrt(Condition_N))
merge3
plot1 <- merge2 %>%
#filter(!Animal== '1371'|!Condition == 'lowOxygen') %>%
group_by(Condition) %>%
ggplot(aes(x= Condition, y = particle_conc, color=Condition)) +
geom_boxplot(colour="black",fill=NA) +
geom_point(aes(text = paste("Animal:", Animal)),
position='jitter',size=3)+
xlab("\nTreatment\n") + # X axis label
ylab("\nExosomes/ml\n") + # Y axis label
ggtitle("GD 17.5 Placental Exosome \nExplant Culture (Ultracentrigution)\n")+ #title
labs(color="Condition") # Label table title
plot1
##Interactive Plot
# ggplotly(plot1)
fit <- t.test(particle_conc ~ Condition,data=merge2)
tidy(fit)